1. Field
The invention relates to self-optimizing method and machine; and more particularly to multivariable, real-time self-optimizing method and machine to determine, with highly fractionally replicated experiments, substantially true main effects of control variables little contaminated by interactions.
2. Prior Art
Computer-controlled, automation systems are widely used. All these systems are extremely powerful. With their uses, consistency is generally achieved together with usually, but not always, associated improved profits, productivity, and product or service qualities (P3Q).
Further, by transferring to machines human intelligence, rather than skill, these systems have ushered us into this Second Industrial Revolution.
But human intelligence or knowledge bases merely document averaged previous test results on old samples with equipment, materials, parts, procedures, or environment different from the present or future. Inherent are errors due to the various samplings, assumptions, or extrapolations, and repeated human interactions. These knowledge bases are often incomplete, inaccurate, biased, erroneous, out-of-date, and too generalized for use in a particular automation task with a specific combination of equipment, procedures, materials, parts, and environment.
Detailed knowledge bases on modern technologies are particularly lacking. Even many old yet important technologies are not fully understood. Each of these technologies involves many processing steps often with unknown chemical, mechanical, electromagnetic, aerodynamic, fluidic, or other phenomena on the subatomic, atomic, microscopic, macroscopic, or other levels. Even the controlling variables in each phenomenon are often not known, and certainly have not been thoroughly studied. In fact, much research and development (RandD) remains to be done in every field. Yet time is running out in this highly competitive world. A new RandD method must, therefore, be developed.
For example, system dynamics of modern processes and equipment are generally ill-defined. Chemical and metallurgical reactions are often uncertain. The mechanism of catalysis is not completely known. Crystal growth is still an art. After millions of controlled welding experiments, the critical welding variables cannot yet be even identified among the very many possible. In the case of the new high-temperature ceramic superconductors, the samples often are still hard to make, shape, purify, reproduce, isolate, stabilize, confirm, or even determine compositions.
Without reliable knowledge bases, the usual automation specialists would be at a loss in selecting the few among many manufacturing or servicing phenomena or variables to be controlled, in formulating the system dynamics models, in setting up the control equations, in determining the control constants, and in specifying setpoints for the control variables.
The fragile and unreliable knowledge bases often give only partial or invalid system dynamics models, oversimplified control equations, and inexact or misleading control constants. In addition, all the setpoints are too arbitrary and round-numbered (e.g., 800xc2x0 C. and not 796.768xc2x0 C., 3 feet per minute, 20 gallons) to be possibly optimal statistically. The chance of these variables or setpoints being optimal at any time, not to say instantaneously or continuously, is nearly zero. Further, the optimal setpoints cannot, by definition, be constant, as is assumed in present automation systems, but must change with variations in time, equipment, procedures, materials, parts, and environment.
The conventional automation systems are also not smart and must be spoon-fed at every step via computer programs or master-slave instructions. They are not totally integrated or automated, and require constant human guidance, interaction, supervision review, and analysis.
The repeated human involvement greatly slows down the conventional automation systems, and passes to machines many defects of the inconsistent and imperfect human test planners, samplers, testers, data collectors and analyzers, communicators, and technicians. Humans are million or billion times slower and less reliable than microprocessors at least in, e.g., memory recalling or storing, information inputting or outputting, data analyzing, communicating, and commanding or actuating.
In addition, usually these present systems merely passively adapt, adjust, correct, control, or regulate, in response to variations in the environment or a few control variables. Dealing with more than several interacting variables results in extremely large number of tests to be made; and in massive amount of data to be collected, conditioned, stored, and quickly or instantly analyzed. This is often impossible because of the well-known problems of xe2x80x9ccombinatorial explosionxe2x80x9d and xe2x80x9ccomputer intractability,xe2x80x9d as shown below.
Yet, modern technologies invariably involve many unpredictable, interacting, and rapidly changing control variables in such categories as: raw materials, vendors, batches, lots, and conditions; compositioning; processing equipment; procedures in each of the many steps in every process; and environment. Many phenomena are transient but highly nonreproducible yet unknown and critical.
Artificial intelligence (AI) technologies, particularly the expert systems, have been developed and increasingly used in various fields. But again the knowledge bases are often inadequate or deficient, particularly on developing technologies. The present expert systems are also costly, inflexible, qualitative, and often inaccurate and out-of-date particularly for complicated yet rapidly improving modern technologies. In addition, they too cannot handle the inherently large number of interacting variables.
Reliable and relevant knowledge base (KB) is scarce and very costly. Up to now, the main bottleneck in the development of expert systems has been the acquiring of the knowledge base in computer-usable form. Human knowledge often not only is fragile, costly, unreliable, but difficult to be translated for uses by machines. Even not in real time, codifying an expert""s limited knowledge has always been a very long and labor-intensive process, if at all possible.
Hence, experts conclude that machine learning is the key to the future of automation in general and expert systems in particular. The machine must first learn through computerized experimentation involving at least 7 (as in steel-making example shown later), 60 (as in a software development shown later), 127 (as in a later case to estimate the computing time), or several hundred and several thousand (as in screening for drugs or chemicals) variables.
The comprehensive but instantly produced knowledge base, not just a few isolated numbers, must be generated in bulk and at low cost. For best results, the new knowledge base must also be computer-coded in real time into, e.g., expert rules for instant use by another machine. The other machine must also select the most relevant knowledge from the comprehensive results, digest and learn from, e.g., the expert rules, and make logical decisions as to which rules or conditions to apply, for the proper and most efficient uses of the knowledge base. This is totally different from merely xe2x80x9ctelecommunicatingxe2x80x9d a few isolated optimum values disclosed in Li""s U.S. Pat. No. 4,472,770 patent. The Li"" self-optimizing method is much more than simple telegraphing words or numbers invented many years ago. So far, however, no such machines exist.
Conventional AI development environments have difficulties in producing efficient real-time systems. This is partly due to the fact that the same code necessary to enhance the development environment slows down the system run-time. To overcome this limitation, AI system designers must embed the knowledge base (KB) into their own custom run-time AI shells to achieve real-time performance. Unfortunately, the deeper the KB is embedded into the actual code, the harder it is to change the KB when maintenance is necessary. The AI system designer must constantly balance system performance versus ease of maintaining and manipulating the KB. An automation system with real-time KB generating capacity is highly desirable.
Present automation systems also invariably contain various hidden errors of samplings, assumptions, extrapolations, scaling-ups, and statistical fluctuations of uncertain magnitudes. These systems have other errors due to, e.g., misused or miscalibrated sensors, imperfect actuators, drifting equipment, and partially damaged components. These errors damage the consistency and uniformity in, e.g., manufacturing or servicing results.
Li in his prior patents on self-optimizing method and machine, e.g., U.S. Pat. Nos. 5,079,690, 4,910,660 4,710,864, 4,472,770, and 4,368,509, presents computerized, automatic research and development (RandD) methods and machines to self-optimize, e.g., for automatically generating new comprehensive knowledge bases on complicated, modern technologies. These patented techniques overcome the common problems of combinatorial explosion and computer intractibility through efficient statistical experimental designs. To handle very large number of control variables always present in modern technologies, the computer-planned test matrices use highly fractionally replicated statistical designs.
These designs test m variables on n tests, in which n may be only m+1. For example, a maximum of m=7 variables at two test levels each may be studied with only n=m+1=8=23, instead of the usual 27=128 tests. This design is, therefore, a xc2xd7xe2x88x923=xc2xd4={fraction (1/16)}th. replicated design. In this case, there are m main effects for the m variables, and one overall average for reference so that m+1=n.
In Li""s prior self-optimizing technology, repeated self-optimizing cycles always use the same test matrix, designed to run always around the previous optimal variable combination. Hence, the main effects of the control variables are always contaminated by the interactions, because the experiments are fractionally replicated. Each main effect shows the effect of a single control variable, on the performance of the object. Each interaction effect gives the combined effects of a plurality of control variables, of the same performance of the object. Further, starting a repeated self-optimizing experimental centered around the previous optimum variable combination narrows down the search to only local optimum values. The desired global optimum point, often much better than the local optimum points, may never be reached no matter how many times the self-optimizing cycles are repeated.
Accordingly, an object of the present invention is to deal with not only the main effects of the control variables, but also the interaction effects of two or more control variables;
Another object of the invention is to separate the main effect of the control variables from selected combined interaction effects of multiple variables;
A yet another object of the invention is to determine the true main effect of selected control variables, uncontaminated by interaction effects of multiple variables in combination;
Yet another object of the invention is to search for the global optimal point, rather than a local optimum point;
A further object of the invention is to provide real-time self-optimizing machine and method capable of handling tens, hundreds, thousands, or more variables with minimum human guidance;
Another object of this invention is to provide close-looped, self-optimizing machine or method which can optimize practically continuously and instantly;
A broad object of the invention is to provide self-optimizing machine or method based on totally self-planned and controlled tests, performed on the very particular machine or method itself without relying on many assumptions, invalid scaling-up laws, and extrapolation from sampled test results obtained on other similar machines or methods; and with practically instant data analyses for timely optimization results;
Yet another object of the invention is to self-optimize in practical or nonideal conditions by tolerating, neutralizing, or suppressing the effect of errors due to defective knowledge bases, miscalibrated sensors, imperfect actuators, drifting equipment, damaged components, and statistical fluctuations;
A further object of the invention is to provide self-optimizing machine or method which operates with deficient and minimal or practically zero knowledge bases, rapidly generates its own new knowledge bases through automatic RandD, and immediately and continuously replaces these new knowledge bases with still newer and more accurate knowledge bases for continuously optimal results;
An additional object of the invention is to provide self-optimizing machine and method which actively computes, and automatically sets at, the instantaneous optimal combinations of the many relevant variables in various categories, with instant feed-back to supply data for immediate replanning, retesting, and reoptimiizng, all without human intervention;
Another object of the invention is to manufacture, in bulk, reliable knowledge bases for the instant development of relevant expert rules or systems for inference engines.
Further objects and advantages of my invention will appear as the specification proceeds.
A method for computer-generating interaction-specific knowledge base for rapidly improving or optimizing a performance of an object comprises performing, according to computer-designed test matrices, at least several computerized automatic RandD cycles on selected control variables. In at least one of the automatic RandD cycles after the first the computer plans a new test matrix designed to minimize or remove at least one expected two-variable interaction effect from a main effect of a single control variable. A machine operating according to the method is also available.